docs: def pca(df, n_components=2, mean_center=False, fcol=None, ecol=None, marker='o', markersize=40, threshold=None, label_threshold=None, label_weights=None, label_scores=None, return_df=False, show_covariance_ellipse=False, *args, **kwargs):
      """
      Perform Principal Component Analysis (PCA) from input DataFrame and generate scores and weights plots.
  
      Principal Component Analysis is a technique for identifying the largest source of variation in a dataset. This
      function uses the implementation available in scikit-learn. The PCA is calculated via `analysis.pca` and will
      therefore give identical results.
  
      Resulting scores and weights plots are generated showing the distribution of samples within the resulting
      PCA space. Sample color and marker size can be controlled by label, lookup and calculation (lambda) to
      generate complex plots highlighting sample separation.
  
      For further information see the examples included in the documentation.
  
      :param df: Pandas `DataFrame`
      :param n_components: `int` number of Principal components to return
      :param mean_center: `bool` mean center the data before performing PCA
      :param fcol: `dict` of indexers:colors, where colors are hex colors or matplotlib color names
      :param ecol: `dict` of indexers:colors, where colors are hex colors or matplotlib color names
      :param marker: `str` matplotlib marker name (default "o")
      :param markersize: `int` or `callable` which returns an `int` for a given indexer
      :param threshold: `float` weight threshold for plot (horizontal line)
      :param label_threshold: `float` weight threshold over which to draw labels
      :param label_weights: `list` of `str`
      :param label_scores: `list` of `str`
      :param return_df: `bool` return the resulting scores, weights as pandas DataFrames
      :param show_covariance_ellipse: `bool` show the covariance ellipse around each group
      :param args: additional arguments passed to analysis.pca
      :param kwargs: additional arguments passed to analysis.pca
      :return:
      """
  
      scores, weights = analysis.pca(df, n_components=n_components, *args, **kwargs)
  
      scores_ax = _pca_scores(scores, fcol=fcol, ecol=ecol, marker=marker, markersize=markersize, label_scores=label_scores, show_covariance_ellipse=show_covariance_ellipse)
      weights_ax = []
      
      for pc in range(0, weights.shape[1]):
          weights_ax.append( _pca_weights(weights, pc, threshold=threshold, label_threshold=label_threshold, label_weights=label_weights) )
      
      if return_df:
          return scores, weights
      else:
          return scores_ax, weights_ax